In the last few years, a number of multi-view semi-supervised learning approaches have been proposed [1]}, [2]}, [3]}, [4]}. Bo et al. (2019) [3]} conducted a simulation study where they compared the performance of the recently published semi-supervised multi-view classification approaches: Auto-weighted Multiple Graph Learning (AMGL)[1]}, Multi-view Learning with Adaptive Neighbors (MLAN) [2]} and Latent Multi-view Semi-Supervised Classification (LMSSC)[3]}. In their study, Bo et al.( 2019) demonstrated that LMSSC was superior to the other algorithms, under different scenarios. The LMSSC approach classifies the test samples in two steps: (a) constructs a graph, with samples as nodes and weighted edges based on similarities among all data-views, and (b) uses label propagation to infer the labels on unlabelled samples. Following the results of Bo et al. (2019), we have compared our proposed S-multi-SNE approach with the LMSSC approach.
